"""
联邦学习客户端实现
基于Flower框架的客户端实现
"""

from typing import List, Tuple, Optional, Dict, Any
import flwr as fl
from flwr.common import Parameters, Scalar
import numpy as np
from enum import Enum

# 定义模型类型枚举（避免循环导入）
class ModelType(Enum):
    """模型类型枚举"""
    CHAT = "chat"
    CODE_GENERATION = "code_generation"
    IMAGE_RECOGNITION = "image_recognition"
    SPEECH_RECOGNITION = "speech_recognition"


class FederatedModelWrapper:
    """联邦学习模型包装器"""
    
    def __init__(self, model_type: ModelType):
        self.model_type = model_type
        
    def get_parameters(self) -> List[np.ndarray]:
        """获取模型参数"""
        # 对于LangChain模型，可能需要特殊处理
        # 这里是示例实现
        return [np.array([])]

    def set_parameters(self, parameters: List[np.ndarray]) -> None:
        """设置模型参数"""
        # 对于LangChain模型，可能需要特殊处理
        pass

    def train(self, train_data, epochs: int = 1) -> Tuple[float, float]:
        """训练模型"""
        # 实现本地训练逻辑
        # 对于LLM，可能是微调过程
        loss = 0.0
        accuracy = 0.0
        # 实际实现需要根据具体模型和数据进行调整
        return loss, accuracy

    def evaluate(self, test_data) -> Tuple[float, float]:
        """评估模型"""
        # 实现评估逻辑
        loss = 0.0
        accuracy = 0.0
        # 实际实现需要根据具体模型和数据进行调整
        return loss, accuracy


class LangChainFederatedClient(fl.client.NumPyClient):
    """LangChain联邦学习客户端"""
    
    def __init__(self, model_type: ModelType, train_data, test_data):
        self.model_wrapper = FederatedModelWrapper(model_type)
        self.train_data = train_data
        self.test_data = test_data

    def get_parameters(self, config: Dict[str, Scalar]) -> List[np.ndarray]:
        """获取模型参数"""
        return self.model_wrapper.get_parameters()

    def fit(self, parameters: List[np.ndarray], config: Dict[str, Scalar]) -> Tuple[List[np.ndarray], int, Dict[str, Scalar]]:
        """训练模型"""
        # 设置模型参数
        self.model_wrapper.set_parameters(parameters)
        
        # 执行本地训练
        epochs = config.get("epochs", 1)
        if isinstance(epochs, int):
            epochs_int = epochs
        else:
            epochs_int = int(epochs) if epochs is not None else 1
            
        loss, accuracy = self.model_wrapper.train(self.train_data, epochs=epochs_int)
        
        # 返回训练后的参数、样本数和指标
        return self.model_wrapper.get_parameters(), len(self.train_data), {"loss": loss, "accuracy": accuracy}

    def evaluate(self, parameters: List[np.ndarray], config: Dict[str, Scalar]) -> Tuple[float, int, Dict[str, Scalar]]:
        """评估模型"""
        # 设置模型参数
        self.model_wrapper.set_parameters(parameters)
        
        # 执行评估
        loss, accuracy = self.model_wrapper.evaluate(self.test_data)
        
        return loss, len(self.test_data), {"accuracy": accuracy}


def start_federated_client(model_type: ModelType, train_data, test_data, server_address: str = "localhost:8080"):
    """启动联邦学习客户端"""
    
    # 创建客户端实例
    client = LangChainFederatedClient(model_type, train_data, test_data)
    
    # 启动客户端
    fl.client.start_numpy_client(server_address=server_address, client=client)